Using Regularized Multi-Task Learning for Schizophrenia MRI Data Classification

Abstract

Background: Machine learning techniques and magnetic resonance imaging methods have been widely used in computer-aided diagnosis and prognosis of severe brain diseases such as schizophrenia, Alzheimer, etc. Methods: In this paper, a regularized multi-task learning method for schizophrenia classification is proposed, and three MRI datasets of schizophrenia, collected from different data centers, are investigated. Firstly, slice extraction is used in image preprocessing. Then texture features of gray-level co-occurrence matrices are extracted from the above processed images. Finally, a p-norm regularized multi-task learning method is proposed to simultaneously learn the site-specific and site-shared features of the multi-site data, which can effectively discriminate schizophrenia patients from normal controls. Results: The classification error rate on 10 datasets can be reduced from 10% to 30%. Conclusions: The proposed method obtains excellent results and provides objective evidence for clinical diagnosis and treatment of schizophrenia.

Classification of schizophrenia based on graph product depth neural network fusion of fMRI and dMRI multidimensional information

“…Because the current research was binary classification, sigmoid was more effective when the feature differences is complex or not particularly large [9]. After comparing of the classifiers, sigmoid was the best classifier in our situation.…”

Constructing the Schizophrenia Recognition Method Employing GLCM Features from Multiple Brain Regions and Machine Learning Techniques

“…Based on these results, it is observed that various classification studies conducted with GLCM features obtained from the left brain have been more successful compared to the literature studies, shown in Table 5. Even though there are studies in the literature that utilize GLCM features [36,37], the data sets used, the number of data points, classifiers, and the analyzed brain regions show variation in these studies. The conducted study analyzed in detail the performance of various classifiers and the textural characteristics of the regions we focused on in the right and left hemispheres for the diagnosis of schizophrenia.…”

“…Based on these results, it is observed that various classification studies conducted with GLCM features obtained from the left brain have been more successful compared to the literature studies, shown in Table 5 . Even though there are studies in the literature that utilize GLCM features [ 36 , 37 ], the data sets used, the number of data points, classifiers, and the analyzed brain regions show variation in these studies.…”